ArticleCBE life sciences education2020
"How Well Does Your Structural Equation Model Fit Your Data?": Is Marcoulides and Yuan's Equivalence Test the Answer?
Article in CBE life sciences education, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
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- Urbanization and Child Development: Investigating Socioeconomic, Structural, and Environmental Influences on Cognitive Development.Developmental science · 2026Article
- Invariance and Construct Validity of HiTOP Dimensions Across Race and Ethnicity in the Adolescent Brain and Cognitive Development (ABCD) Study.Assessment · 2025Article
- Racial/Ethnic Discrimination and Internalizing Symptoms in Ethno-Racially Minoritized Youth: Exploring the Influence of Emotion Dysregulation.Child psychiatry and human development · 2025Article
- Effects of biopsychosocial complexity and pain-related factors on opioid prescription in patients with chronic musculoskeletal pain.Pain reports · 2025Article
- Structural Equation Modelling of Retinopathy of Prematurity Treatment Integrating Both Physical and Clinical Effects.Journal of clinical medicine · 2025Article
- The parallel mediation effects of nutrition, physical activity on depression and sarcopenia risk among older people with diabetes.Frontiers in public health · 2025Article
- The Answer is "No": A Comment on Peugh and Feldon (2020).CBE life sciences education · 2024Article
- What Makes a Good Match? Predictors of Quality Mentorship Among Doctoral Students.CBE life sciences education · 2024Article
- Central Executive and Default Mode Networks: An Appraisal of Executive Function and Social Skill Brain-Behavior Correlates in Youth with Autism Spectrum Disorder.Journal of autism and developmental disorders · 2024Article
- Patient satisfaction with nursing care in infertility patients: A questionnaire survey.Global health & medicine · 2024Article
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- Validating part of the social media infodemic listening conceptual framework using structural equation modelling.EClinicalMedicine · 2024Article
- Individuals' contribution to household energy savings: The role of big-two personality traits.Heliyon · 2024Article
- Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool.Heliyon · 2024Article
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2 authors.
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Abstract
Structural equation modeling is an ideal data analytical tool for testing complex relationships among many analytical variables. It can simultaneously test multiple mediating and moderating relationships, estimate latent variables on the basis of related measures, and address practical issues such as nonnormality and missing data. To test the extent to which a hypothesized model provides an appropriate characterization of the collective relationships among its variables, researchers must assess the "fit" between the model and the sample's data. However, interpreting estimates of model fit is a problematic process. The traditional inferential test of model fit, the chi-square test, is biased due to sample size. Fit indices provide descriptive (i.e., noninferential) values of model fit (e.g., comparative fit index, root-mean-square error of approximation), but are unable to provide a definitive "acceptable" or "unacceptable" fit determination. Marcoulides and Yuan have introduced an equivalence-testing technique for assessing model fit that combines traditional descriptive fit indices with an inferential testing strategy in the form of confidence intervals to facilitate more definitive fit conclusions. In this paper, we explain this technique and demonstrate its application, highlighting the substantial advantages it offers the life sciences education community for drawing robust conclusions from structural equation models. A structural equation model and data set (
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